Human readers have been investigating and analyzing mammographic abnormalities for the past 30 years. Microcalcifications are the most important symptom in the identification of carcinomas on mammograms. Readers have attempted to characterize calcifications for the past 30 years in an effort to differentiate visually benign from malignant genesis. The literature provides examples of a large variety of descriptors of morphology and distribution of breast calcifications. Several of these descriptors have been linked to likelihood of malignancy and can be used as indicators of suspiciousness. A summary of the descriptors reported to-date is presented in the following Tables 1 and 2. Table 1 lists the forms, including shape, morphology and distribution of the individual microcalcifications listed in the clinical literature that suggest benign or malignant disease. Table 2 lists the intensity and group descriptors of calcifications listed in the clinical literature.
TABLE 1Probable Genesis TypeB: Probably BenignM: Suggestive of MalignancyForm DescriptorU: UncertainLinearMBranching or V, W, X, Y, Z shapesMSmall numerous irregularUPunctiform in a monomorphic groupBPunctiform in a monomorphic group or ofMvarying sizeAngularUSmooth denseBHollow or ring or radiolucent or eggshellBAnnularBFine with major variations or very fine,M or Uhardly visibleWorm-likeMBean formMUndulating line of various lengthsMAmorphousURoundish or facetedBTea cup-likeBClumpy with rounded edgesBOvoidB
TABLE 2Probable Genesis TypeB: Probably BenignM: Suggestive of MalignancyIntensity and Group descriptorsU: UncertainBlurred contoursBPaleBPolymorphic groupMMonomorphic groupBSmall, clusteredMLinear tubular in parallel tracks (vascular)B
A visual system of differential diagnosis based on the morphological properties of single and grouped calcifications has shown to lead to 97.6% sensitivity (correct identification of cancers) and 73.3% specificity (correct identification of benign cases). From this visual system to the establishment of the Breast Imaging Reporting and Data System (BIRADS) lexicon of the American College of Radiology (ACR) in 1993, it is apparent that morphology is one of the most important clinical factors/aids in making the diagnosis of calcifications.
The development of the BIRADS categories for calcifications was based on several of the characteristics listed in Tables 1 and 2. Several formulations were modified and new terms were assigned to better and more generally describe the calcification forms and distributions. The recommended descriptors of the morphology and distribution of the calcifications in the Lexicon are listed in Table 3 for easy reference and comparison. The number of calcifications present is not by itself a clear indicator of benign or malignant disease but combined with other characteristics may increase or decrease suspiciousness.
TABLE 3BIRADS descriptors for calcifications with associated genesis type(B: probably benign; M: suggestive of malignancy; U: Uncertain)Morphology or characterSkin (lucent centered)BVascular (linear tubular with parallelBtracks)Coarse or popcorn likeBLarge rod-likeBRound (larger than 0.5 mm)BEggshell or rim (thin walled lucentBcentered, cystic)Milk of calcium (varying appearanceBin projections)Dystrophic (irregular in shape,Bover 0.5 mm, lucent centered)Punctate (round smaller than 0.5 mm)BSuture (linear or tubular with knots)BSpherical or lucent center (smoothBand round or oval)Amorphous or indistinctUPleomorphic or heterogeneousMgranularFine linearMFine linear branchingMDistributionClusteredUSegmentalU/MURegionalUDiffuse/ScatteredBLinearMNumber1-5U5-10U>10U
A radiologist makes the final diagnosis of the detected calcifications based on the BIRADS characteristics, demographic information, and associated mammographic findings. However, inter- and inner-observer variability in the assignment of categories or morphological features to the identified calcifications and ambiguity in the interpretation degrades significantly diagnostic performance. Hence, successful differentiation is limited among radiologists and can be as low as 20%. Computer algorithms can assist the radiologist in the diagnostic task with methods that translate and automate the clinical experience.